FearNot! An Anti-Bullying Intervention: Evaluation of an Interactive Virtual Learning Environment

Original paper can be found at: http://www.aisb.org.uk/publications/proceedings.shtm

Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability

The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques

Acoustic, psychophysical, and neuroimaging measurements of the effectiveness of active cancellation during auditory functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is one of the principal neuroimaging techniques for studying human audition, but it generates an intense background sound which hinders listening performance and confounds measures of the auditory response. This paper reports the perceptual effects of an active noise control (ANC) system that operates in the electromagnetically hostile and physically compact neuroimaging environment to provide significant noise reduction, without interfering with image quality. Cancellation was first evaluated at 600 Hz, corresponding to the dominant peak in the power spectrum of the background sound and at which cancellation is maximally effective. Microphone measurements at the ear demonstrated 35 dB of acoustic attenuation [from 93 to 58 dB sound pressure level (SPL)], while masked detection thresholds improved by 20 dB (from 74 to 54 dB SPL). Considerable perceptual benefits were also obtained across other frequencies, including those corresponding to dips in the spectrum of the background sound. Cancellation also improved the statistical detection of sound-related cortical activation, especially for sounds presented at low intensities. These results confirm that ANC offers substantial benefits for fMRI research

Crop identification technology assessment for remote sensing (CITARS). Volume 10: Interpretation of results

The CITARS was an experiment designed to quantitatively evaluate crop identification performance for corn and soybeans in various environments using a well-defined set of automatic data processing (ADP) techniques. Each technique was applied to data acquired to recognize and estimate proportions of corn and soybeans. The CITARS documentation summarizes, interprets, and discusses the crop identification performances obtained using (1) different ADP procedures; (2) a linear versus a quadratic classifier; (3) prior probability information derived from historic data; (4) local versus nonlocal recognition training statistics and the associated use of preprocessing; (5) multitemporal data; (6) classification bias and mixed pixels in proportion estimation; and (7) data with differnt site characteristics, including crop, soil, atmospheric effects, and stages of crop maturity

Technological Support for a Learning Oriented Knowledge Management System

Knowledge management is quickly becoming a requirement for today’s complex organizations. Creating and managing existing knowledge has been linked to successful innovation and to sustainable competitive advantage. However, systems specifically designed to manage knowledge, support knowledge creation, and verify existing knowledge are in their infancy. This article follows the framework for a Learning-Oriented Knowledge Management System, and shows how such a complex system can be supported by an equally complex technology – that is, a multi-agent system. We define single agents and multi-agent systems and subsystems in the context of knowledge management systems in general, and the Learning-Oriented Knowledge Management System (LOKMS) specifically. We show how a multi-agent system can be conceived to fully support the LOKMS, describe some necessary agents and agent subsystems, and demonstrate prototypically a multi-agent system designed and built to support the integrity-checking component of the LOKMS. This system begins the process of LOKMS design and development

Minsky machines and algorithmic problems

This is a survey of using Minsky machines to study algorithmic problems in semigroups, groups and other algebraic systems.Comment: 19 page

An efficient method of evaluating multiple concurrent management actions on invasive populations

Evaluating the efficacy of management actions to control invasive species is crucial for maintaining funding and to provide feedback for the continual improvement of management efforts. However, it is often difficult to assess the efficacy of control methods due to limited resources for monitoring. Managers may view effort on monitoring as effort taken away from performing management actions. We developed a method to estimate invasive species abundance, evaluate management effectiveness, and evaluate population growth over time from a combination of removal activities (e.g., trapping, ground shooting) using only data collected during removal efforts (method of removal, date, location, number of animals removed, and effort). This dynamic approach allows for abundance estimation at discrete time points and the estimation of population growth between removal periods. To test this approach, we simulated over 1 million conditions, including varying the length of the study, the size of the area examined, the number of removal events, the capture rates, and the area impacted by removal efforts. Our estimates were unbiased (within 10% of truth) 81% of the time and were correlated with truth 91% of the time. This method performs well overall and, in particular, at monitoring trends in abundances over time. We applied this method to removal data from Mingo National Wildlife Refuge in Missouri from December 2015 to September 2019, where the management objective is elimination. Populations of feral swine on Mingo NWR have fluctuated over time but showed marked declines in the last 3–6 months of the time series corresponding to increased removal pressure. Our approach allows for the estimation of population growth across time (from both births and immigration) and therefore, provides a target removal rate (above that of the population growth) to ensure the population will decline. In Mingo NWR, the target monthly removal rate is 18% to cause a population decline. Our method provides advancement over traditional removal modeling approaches because it can be applied to evaluate management programs that use a broad range of removal techniques concurrently and whose management effort and spatial coverage vary across time

Mobility of thorium ions in liquid xenon

We present a measurement of the $^{226}$Th ion mobility in LXe at 163.0 K and 0.9 bar. The result obtained, 0.240$\pm$0.011 (stat) $\pm$0.011 (syst) cm$^{2}$/(kV-s), is compared with a popular model of ion transport.Comment: 6.5 pages,

Boundary of two mixed Bose-Einstein condensates

The boundary of two mixed Bose-Einstein condensates interacting repulsively was considered in the case of spatial separation at zero temperature. Analytical expressions for density distribution of condensates were obtained by solving two coupled nonlinear Gross-Pitaevskii equations in cases corresponding weak and strong separation. These expressions allow to consider excitation spectrum of a particle confined in the vicinity of the boundary as well as surface waves associated with surface tension.Comment: 6 pages, 3 figures, submitted to Phys.Rev.